Introduction: SCLC accounts for almost 15% of lung cancer cases in the United States. Nomogram prognostic models could greatly facilitate risk stratification and treatment planning, as well as more refined enrollment criteria for clinical trials. We developed and validated a new nomogram prognostic model for SCLC patients using a large SCLC patient cohort from the National Cancer Database (NCDB). Methods: Clinical data for 24,680 SCLC patients diagnosed from 2004 to 2011 were used to develop the nomogram prognostic model. The model was then validated using an independent cohort of 9700 SCLC patients diagnosed from 2012 to 2013. The prognostic performance was evaluated using p value, concordance index and integrated area under the (time‐dependent receiver operating characteristic) curve (AUC). Results: The following variables were contained in the final prognostic model: age, sex, race, ethnicity, Charlson/Deyo score, TNM stage (assigned according to the American Joint Committee on Cancer [AJCC] eighth edition), treatment type (combination of surgery, radiation therapy, and chemotherapy), and laterality. The model was validated in an independent testing group with a concordance index of 0.722 ± 0.004 and an integrated area under the curve of 0.79. The nomogram model has a significantly higher prognostic accuracy than previously developed models, including the AJCC eighth edition TNM‐staging system. We implemented the proposed nomogram and four previously published nomograms in an online webserver. Conclusions: We developed a nomogram prognostic model for SCLC patients, and validated the model using an independent patient cohort. The nomogram performs better than earlier models, including models using AJCC staging.

中文翻译：

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小细胞肺癌患者列线图预后模型的开发和验证


简介：在美国，SCLC 几乎占肺癌病例的 15%。列线图预后模型可以极大地促进风险分层和治疗计划，以及更细化的临床试验入组标准。我们使用国家癌症数据库 (NCDB) 中的大型 SCLC 患者队列开发并验证了 SCLC 患者的新列线图预后模型。方法：使用 2004 年至 2011 年诊断的 24,680 名 SCLC 患者的临床数据来开发列线图预后模型。然后使用 2012 年至 2013 年诊断的 9700 名 SCLC 患者组成的独立队列对该模型进行验证。使用 p 值、一致性指数和（时间依赖性受试者工作特征）曲线下积分面积 (AUC) 评估预后表现。结果：最终的预后模型中包含以下变量：年龄、性别、人种、民族、Charlson/Deyo 评分、TNM 分期（根据美国癌症联合委员会 [AJCC] 第八版分配）、治疗类型（联合手术、放射治疗和化疗）和偏侧性。该模型在独立测试组中得到验证，一致性指数为 0.722 ± 0.004，曲线下积分面积为 0.79。列线图模型比以前开发的模型（包括 AJCC 第八版 TNM 分期系统）具有显着更高的预后准确性。我们在在线网络服务器中实施了提议的列线图和之前发布的四个列线图。结论：我们开发了 SCLC 患者列线图预后模型，并使用独立患者队列验证了该模型。该列线图的性能优于早期模型，包括使用 AJCC 分期的模型。